In addition, we make use of three more variants of ImageNet: Stylized ImageNet, ImageNet-A and ImageNet-R. Stylized ImageNet (SIN) is a variant of ImageNet which is obtained using style transfer. It has been first introduced to induce a shape bias in convolutional networks [1]}.
ImageNet-A [2]} is a test set of \(7\,500\)  natural but adversarially collected images with, e.g., unusual backgrounds or occlusions for 200 ImageNet classes.
ImageNet-R [3]} is a test set of \(30\,000\)  image renditions (e.g., paintings, sculptures, embroidery) for another set of 200 ImageNet classes.
When evaluating on these datasets, we only use the classes intersecting with ImageNet-100.
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